tinker_cookbook.preference.Config

class tinker_cookbook.preference.Config()

Configuration for Direct Preference Optimization (DPO) training.

This is a chz dataclass that holds all hyperparameters, infrastructure settings, and checkpointing options for a DPO training run.

config = Config(
    log_path="~/logs/dpo_run",
    model_name="Qwen/Qwen3.5-9B",
    renderer_name="qwen3_5_disable_thinking",
    dataset_builder=my_dpo_dataset_builder,
    dpo_beta=0.1,
    learning_rate=1e-5,
)
main(config)

Fields:

• log_path ( str)
• model_name ( str)
• recipe_name ( str)
• dataset_builder ( ChatDatasetBuilder)
• load_checkpoint_path ( str | None, default: None)
• renderer_name ( str | None, default: None)
• learning_rate ( float, default: 1e-05)
• lr_schedule ( LRSchedule, default: 'linear')
• num_epochs ( int, default: 1)
• dpo_beta ( float, default: 0.1)
• lora_rank ( int, default: 32)
• num_replicas ( int, default: 8)
• base_url ( str | None, default: None)
• evaluator_builders ( list[EvaluatorBuilder], default: []) – Checkpointing and evaluation (0 = disabled for *_every fields)
• infrequent_evaluator_builders ( list[EvaluatorBuilder], default: [])
• save_every ( int, default: 20)
• eval_every ( int, default: 10)
• infrequent_eval_every ( int, default: 100)
• ttl_seconds ( int | None, default: 604800) – 7 days
• rolling_save_every ( int, default: 0) – but skips the sampler-weight export, making it cheaper than periodic checkpoints.
• rolling_ttl_seconds ( int, default: 7200) – 2 hours
• adam_beta1 ( float, default: 0.9)
• adam_beta2 ( float, default: 0.95)
• adam_eps ( float, default: 1e-08)
• wandb_project ( str | None, default: None)
• wandb_name ( str | None, default: None)
• enable_trace ( bool, default: False) – Profiling
• span_chart_every ( int, default: 0)
• reference_model_name ( str | None, default: None)
• max_steps ( int | None, default: None) – Maximum number of training steps. If None, train for num_epochs * n_batches.